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oscillation analyses • Practical Experience of deploying state-of-the-art machine learning techniques Desirable: • Ability to develop and apply new concepts • Verbal and written communication skills • Ability
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(segmentation analysis by machine learning) and automatic language processing on large quantities of digitised historical photographs and their metadata. - management, enrichment and structuring of project data
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processing, involving machine learning techniques, as well as active participation in data collection from the detectors deployed on site. - Analysis of particle physics data applied to muography: filtering
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(FL) is a promising paradigm that is gaining grip in the context of privacy-preserving machine learning for edge computing systems [1]. Thanks to FL, several data owners called clients (e.g
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], e-learning robotics based on human gestures [6] or active exoskeletons [7]. The aim of this thesis is therefore to understand human-human collaboration strategies in order to develop a coherent
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the field of algorithm configuration and selection in a streaming fashion by investigating techniques that continuously optimize machine learning models as new data instances arrive [2]. A key focus will be
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-I) for the NASA HWO project. Additionally, you will benefit from the support of the Machine Learning “Centre de données Astrophysiques de Marseille” (CeSAM). The Laboratoire d'Astrophysique de
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selection, the marginal likelihood, and generalization. In International Conference on Machine Learning, pages 14223–14247. PMLR, 2022. [8] B. O. Muth ́en. Beyond SEM: General latent variable modeling
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particular to optimise the design of the instrument for maximum spatial resolution; ii) evaluate the contribution of machine learning and deep learning methods to jointly estimate the 3D distribution of GP
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and machine learning Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8254-SYLDES-005/Default.aspx Work Location(s) Number of offers available1Company/InstituteLaboratoire